We describe a prototype application that uses Bayesian
statistics to improve Trans Alaska Pipeline System (TAPS)
controller response time and reduce control room workloads
by automating the process of evaluating and attributing the
causes of Pipeline Leak Detection (PLD) system alarms. Pipeline Leak Detection System for Alarm Validation and
Evaluation (PLDSAVE) was developed with the support of
Alyeska Pipeline Service Company (APSC), operator of
TAPS, and is designed to work in conjunction with the
existing TAPS leak detection system. The basis of the TAPS PLDSAVE application is an underlying
probabilistic description of TAPS. This probabilistic
description encompasses instrument and PLD system
modeling errors, a leak condition statistical model and a
description of the physical constraint conditions applicable to
TAPS configuration. The stochastic model raw data is TAPS'
instrument measurements, PLD system state information, plus
modeling data and leak alarms provided by the PLD system. A Bayesian inference process is regularly applied to this
stochastic model in such a way that the system determines a
leak probability or validity value, and the most likely current
TAPS leak and measurement/modeling error state. From the
user perspective, any PLD system alarm is accompanied by
the calculated leak probability value combined with an
estimate of the current measurement and modeling error
magnitudes that apply to the PLD system if the system is
assumed not to be in a leak condition. The goal of PLDSAVE is that TAPS safety and integrity are
therefore enhanced as minimizing cost and operational
distraction by (1) allowing the controller to respond in shorter
time frames and assertively to high probability leak
conditions, and (2) by assisting the user in the PLD system
alarm attribution process by pointing to the most likely
instrumentation or modeling alarm causes and conditions
when a leak is of low assessed probability.